Difference-in-Difference Estimation (online)

Course structure

This module is an introduction into one of the most common quasi-experimental research design in Social Sciences: Difference-in-Difference (Diff-inDiff, or DiD). DiD can be applied to a variety of policy contexts, including “natural experiments” (e.g. the unexpected event of Covid19).

The module aims to familiarise students with the method per se, but also with the conditions that may induce researchers for using this particular data analysis, learning specific pros and cons.

Each session will be mirrored by a practical workshop seminar where students will put the analytical techniques introduced in the lectures. Students will learn how to analyse some specific datasets prepared for the course, using Stata. Datasets will be available for free over Moodle relevant page. Students may bring their own dataset they are working on, provided data comprises also some time series (very minimal condition is having data about two distinct moments).

By the end of the course, students should be able to use Difference-in-Difference technique to estimate whether a specific event or policy actually had an effect on a target population, which effect (positive or negative), and the respective size of it, making comparison to a “control group”.

Event outline

The course will take place online on Zoom on 23rd March, from 09:30am until 4:30pm.

Session 1

• 09:30 – 11:00 Why we wish to be in the position to devise a quasi-experimental research (live synchronous); What Diff-in-Diff is, when to use it, when not to use it; some basic examples
• 11:00 – 12:30 Computer workshop 1. Students work through worksheet at their own pace

• 12:30 – 13:30 Lunch break.

Session 2

• 13:30 – 15:00 Examples of how to put oneself in the position to use Diff-in-Diff, and when this is to be avoided – the research design applied to a research question / policy evaluation (live synchronous)
• 15:00 – 16:30 Computer workshop 2. Students work through datasets.

The course covers

• Why a quasi-experimental research is the best scenario for us
• How can we put ourselves in that position
• What Diff-in-Diff promises you, its strength and pitfall
• How to pre-test whether it is suitable
• Doing Diff-in-Diff and reading results, in Stata
• Understanding and accounting for complex research design – this may include “data science”
• How to release and communicate the most robust findings.

Learning outcomes

By the end of the course participants will:

•  Understand the strengths and limitations of Diff-in-Diff.
•  Understand key aspects this specific analysis.
•  Be able to approach one’s area of interest with this technique.

Course fees

The price per teaching day is:

• £30 per day for students registered at UK/EU University
• £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff and staff at UK/EU registered charity organisations and recognised UK/EU research institutions
• £100 per day for all other participants.

In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. No refunds are available after this date.

If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs.

The University of Southampton’s Online Store T&Cs also continue to apply.

Preparatory reading 

Material will be sent to participants in compliance with copyright before the course day. Consulting this material is recommended, but not mandatory.

Handbook:

• Wooldridge J. M. (2010) Econometric Analysis of Cross Section and Panel Data, The MIT Press.

Empirical works:

• Marini, G., Meschitti V. (2023). Do funding schemes help ameliorate publications? An analysis among Italian academics who won FIRB and ERC. Sociologia del Lavoro [forthcoming]
• Marini, G. (2022). International co-authored publications: The effect of joining the European Union or being part of the European Research Area. Hungarian Educational Research Journal. doi:10.1556/063.2022.00192
• Marini, G., & Yang, L. (2021). Globally Bred Chinese Talents Returning Home: An Analysis of a Reverse Brain-Drain Flagship Policy. Science and Public Policy. doi:10.1093/scipol/scab021
• Marini, G., & Locke, W. (2021). The rapid increase in faculty from the European Union in UK higher education institutions and the possible impact of Brexit. International Faculty in Asia in comparative global perspective. Springer. doi:10.1007/978-981-33-4980-3
• Marini, G. (2018). Higher education staff and Brexit. Is the UK losing the youngest and brightest from other EU countries?. Tertiary Education and Management. doi:10.1080/13583883.2018.1497697

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